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The Data Acquisition, Architecture & Management Layer | Part 5 of The Data Strategy Framework Series

Written by Piotr Sokolowski | May 8, 2025

This extract is part five of a five-part series that breaks down the layers of the Data Strategy Framework for Connected Medical Devices, which we introduced in our recent whitepaper. Featuring insights from industry leaders at Cochlear, Dexcom, Medtronic, Omron, Vocxi Health, and S3 Connected Health, the paper offers a practical framework for leveraging data to stay competitive and succeed in a data-driven healthcare landscape. 

The data acquisition, architecture, and management layer form the backbone of any data strategy framework, focusing on data collection, structuring, storage, and management.  

This layer is crucial for medical device vendors, particularly those involved in developing smart connected patient devices, as it ensures the reliable functioning of the devices and the secure management of the vast amounts of data they generate. It supports both the real-time and long-term data requirements of medical devices, enabling them to deliver critical services and insights to medtech organizations, healthcare providers, and patients. 

"Over time, data quality can degrade, especially when devices leverage artificial intelligence, potentially leading to unsatisfactory device performance. It's vital to have robust data quality management to ensure devices continue to function effectively over time. This includes monitoring data quality continuously, as changes at the system's boundaries or integrating with other systems may affect the solution's overall effectiveness."

Aghogho Ekpruke, Research & Development, Medtronic 

A critical challenge for companies developing smart connected patient devices is standardizing data management practices. The cost of managing data extends far beyond just collection — it involves ongoing service and support, storage, system management, and maintaining these processes over extended service periods. Without comprehensive planning, the costs and complexities of managing data can spiral, leading to inefficiencies and potentially undermining the success of the device. To avoid these pitfalls, companies must develop a strategic approach that accounts for the long-term needs of data management, including how data will be stored, accessed, and protected over time. 

“The total cost of managing data involves more than just collection. It includes ongoing service and support, storage, data system management, and maintaining longer service periods. Comprehensive planning is necessary to account for these costs and ensure data remains manageable and valuable.” 

Bill Betten, Director of Medtech Solutions, S3 Connected Health 

This strategic layer encompasses data acquisition methods, transmission technologies, and the underlying architecture that stores and processes data for future retrieval, analysis, and operational use. When effectively defined and implemented, it provides:  

 

  • Reliable and secure data collection: Ensures that data is captured accurately and in real time from medical devices with minimal loss. 
  • Scalable data architecture: Capable of handling large volumes of structured and unstructured data while providing efficient access and retrieval. 
  • High-performance data pipelines: Systems that allow for fast, real-time data transmission and processing, essential for devices requiring immediate action. 
  • Robust data storage and backup: Ensures data integrity and availability, even in cases of hardware failures or security breaches. 
  • Optimized data flows: Efficient ingestion and processing of raw data to enable real-time analytics and decision-making. 

 

 

Key functions 

Connectivity and data transmission  

For medical devices — especially smart connected patient devices — connectivity and data transmission are critical. These devices typically rely on IoT. technologies to transmit data, which must be handled reliably with minimal delays. This requires careful management of bandwidth and ensuring seamless connectivity.  

 

Data security   

Data security is paramount in medical devices, as the data they generate is often sensitive and personal. Protecting patient data through encryption, access control, and compliance with privacy regulations like HIPAA and GDPR is essential to maintaining trust and meeting legal requirements.  

 

Data acquisition  

Data acquisition involves capturing data from smart connected patient devices, often in real time. Systems must be designed to ensure that data is collected securely, accurately, and with minimal latency.  

 

Data ingestion and integration  

Once data is collected, it must be ingested into centralized storage systems, often in real time, where it can be integrated with other relevant data sources such as EHRs. This integration creates a comprehensive view of patient health and device performance.  

 

Data architecture  

The data architecture design is essential for efficiently storing, retrieving, and managing the data generated by medical devices. The architecture must accommodate various data formats — both structured and unstructured — and ensure the accessibility and security of the data.  

 

Device reliability and data continuity   

Data continuity is essential for medical devices, particularly when dealing with critical patient information. Devices must have backup systems in place to manage any data interruptions or disconnections.  

Data processing and transformation  

Once data is acquired, it must be processed and transformed into a usable format. This involves cleansing the data, normalizing different formats, and ensuring consistency across all sources.   

 

Interoperability and standardization  

Medical devices often need to communicate with other systems, such as EHRs, which requires interoperability between devices and systems. Ensuring that data can flow seamlessly between different systems often requires adherence to protocols such as HL7 or FHIR.  

 

Data management (Storage, backup, and recovery)  

Managing how data is stored, backed up, and recovered in the event of failure is crucial for medical device companies. Loss of data could severely impact patient care or lead to regulatory compliance issues.  

 

Scalability and performance  

Medical devices generate massive amounts of data in real time, posing significant challenges in terms of storage, bandwidth management, and processing. Without scalable architecture, these data streams could overwhelm systems, leading to delays or data loss. 

 

The data strategy should align with device functionality and constraints 

The functionality or constraints of smart connected patient devices will also influence data strategy, as failing to align the two can result in inefficiencies and missed opportunities. Many companies falter because they overlook the need to tailor their data strategies according to the device's capabilities and limitations.

Data retrieval, data volume, data value, and data frequency are core components that should be carefully considered. If these elements are ignored, data collection efforts may yield information that isn’t valuable or is too difficult to retrieve and use effectively. The focus should always be on ensuring that data collection methods and the device's functional design are aligned. 

The technical complexities of managing healthcare data further emphasize the importance of specialized expertise. O'Gorman notes, "collecting data from devices involves complexities with firmware, hardware, and connectivity," which requires careful attention to how data is generated, retrieved, stored, and managed.

These challenges are compounded by the need to differentiate between structured and unstructured data and ensure that the insights generated are valuable. Without skilled professionals to navigate these complexities, medtech companies risk losing the ability to extract meaningful insights from the data they collect. 

Although these aspects may seem relevant only to product engineering, collaboration among all stakeholders throughout the development cycle is critical to shaping a viable data acquisition strategy. Data analysts need to work closely with engineers and product developers to understand the practical limitations of the device, including the layers of firmware, hardware, and software that make data collection and transfer possible.

By understanding these constraints, teams can develop strategies focused on capturing meaningful and actionable data rather than overwhelming product design—or, worse, failing to capture the required data. When these groups work in silos, the device’s real-world functionality may not align with data goals, leading to gaps in effectiveness. 

"It’s critical to consider functional aspects of data, like battery life, charging the device, and retrieving your data" to avoid complications down the line.”  

Aghogho Ekpruke, Research & Development, Medtronic 

Betten emphasized several essential and practical considerations for the successful development of data-transmitting smart connected patient devices. The device environment is crucial, as devices need to operate reliably across varied settings, from clinical spaces to home environments, often alongside other equipment.

Clear data requirements help focus on relevant information, preventing unnecessary complexity. Battery life is vital for extended use, requiring careful energy management to minimize recharging needs. Connectivity shapes device functionality by enabling reliable data transmission while robust security safeguards sensitive patient data. User experience also plays a key role; devices must be comfortable and intuitive for sustained use and should limit user actions for data exchange. Finally, efficient data transmission balances power use with timely updates, ensuring devices remain practical and effective. Together, these factors ensure smart connected patient devices meet technical, user, and regulatory needs.

 

Checklist for establishing practicalities of retrieving data  

 

Device environment 

  • Will the device operate in a controlled (e.g. hospital) or uncontrolled (e.g. home) environment, and what impact will this have on data collection? 
  • How will it coexist with other nearby devices? Could interference or co-location with other wireless devices affect data integrity? 
  • Will the device need to integrate with other devices (e.g. for closed-loop therapy delivery) or external systems (e.g. EHRs)? 

 

 

Data requirements 

  • What types of data are essential to collect, and what level of accuracy is required? 
  • How frequently must the data be collected and transmitted? Should it be real-time or periodic, and how does this impact data latency and throughput needs? 
  • Is the data unidirectional (from the device to external systems) or bidirectional (requiring input back to the device)? 

 

 

Data transmission frequency and efficiency 

  • What is the optimal frequency for data transmission to balance battery efficiency and timely data delivery? 
  • Should data be transmitted continuously, in real-time, or in batch intervals? How will this impact device performance and battery life? 
  • How will data prioritization be managed, ensuring that critical data is transmitted first, if bandwidth is limited? 

 

 

Battery life and power management 

  • How long must the device function on a single battery cycle or charge, and does it align with its intended use (e.g. continuous vs. intermittent monitoring)? 
  • What strategies can be employed to reduce power consumption (e.g. optimizing data transmission frequency or using custom-designed ultra-low-power communication protocols)? 
  • How will critical functions (e.g. monitoring) be prioritized in case of low battery power? 

 

 

Connectivity options 

  • What connectivity standards (e.g. Bluetooth, Wi-Fi, cellular) are best suited to ensure reliable transmission in the intended environment? 
  • Will data be sent directly to an external network or through an intermediary device, such as a mobile phone or dedicated hub? If a hub is required, what will it be? 
  • Are there anticipated issues with network availability or connectivity stability, and how will these be mitigated? 

 

 

Security and privacy 

  • What encryption standards are needed to protect data both in transit and at rest, given the sensitivity of patient information? 
  • How will the device handle data breaches or cyber threats? Are there protocols for detecting and mitigating such risks? 
  • Does the device meet all regulatory requirements for data security, such as HIPAA or GDPR? 

 

 

User experience 

  • How can the device be designed for comfort and ease of use in long-term wear scenarios? 
  • What will the maintenance or charging routine look like for users, and how can it be simplified? 
  • Are there intuitive, user-friendly instructions for managing connectivity and troubleshooting issues? 

 

Check out our recent whitepaper for more information on building an effective data strategy framework for connected medical devices. Featuring insights from industry leaders at Cochlear, Dexcom, Medtronic, Omron, Vocxi Health, and S3 Connected Health, the paper offers a practical framework for leveraging data to stay competitive and succeed in a data-driven healthcare landscape. You can read other extracts in this series to understand each layer better: